Heterogeneous behavioral mechanisms in epidemiological models

2026-06-12

AI Summary

Traditional epidemiological models often yield biased predictions because they neglect heterogeneity in individual risk perception and behavioral responses. This study proposes a unified transmission framework that explicitly incorporates behavioral heterogeneity by categorizing the population into risk-neutral and risk-averse groups, each exhibiting distinct contact behaviors. For the first time in epidemiology, a Bayesian mixture approach is introduced to characterize how such behavioral diversity shapes epidemic dynamics. Built upon an extended SIR structure, the model integrates simulation and empirical analysis, demonstrating superior performance over conventional approaches in parameter recovery, trajectory fitting, and forecasting accuracy. It effectively mitigates common issues such as underestimation of infection peaks and artificial elongation of epidemic curves, thereby enhancing both behavioral realism and predictive reliability.

Abstract

Traditional epidemic models frequently assume behavioral homogeneity. The susceptible-infected-recovered model provides a robust foundation for characterizing disease transmission, but it does so without accounting for how people actually respond to risk. In contrast, behavioral change models incorporate mechanisms that capture how individuals adjust their actions during an outbreak, recognizing that rising infection risk typically motivates protective behaviors. Yet both approaches share a key limitation: they overlook the inherent heterogeneity of a population. In reality, communities are a complex mixture of risk tolerances and behavioral tendencies. Ignoring this inherent heterogeneity can obscure important differences in how individuals perceive and respond to disease threats. This paper introduces a novel Bayesian mixture model designed to address this limitation by partitioning the population into two distinct behavioral patterns: risk-neutral individuals, who maintain baseline contact rates, and risk-averse individuals, who modulate their behavior in response to epidemic severity. By integrating these disparate dynamics into a unified transmission framework, the proposed model explicitly accounts for varying population behaviors often overlooked by aggregate approaches. Through simulation studies and empirical data applications, we demonstrate that this approach significantly outperforms traditional models in parameter recovery, epidemic trajectory estimation, and forecasting precision. The findings suggest that failing to account for behavioral diversity leads to biased peak estimates and artificially stretched epidemic curves. Consequently, this research provides a more nuanced computational toolkit for predicting outbreak trajectories in socially fragmented environments, ensuring that public health intervention strategies are informed by a foundation of behavioral realism.

Problem

• behavioral heterogeneity
• epidemic modeling
• risk perception
• population diversity
• disease transmission

Innovation

• Bayesian mixture model
• behavioral heterogeneity
• epidemic modeling
• risk-averse behavior
• transmission dynamics